Partition-based index management in hadoop-like data stores

ABSTRACT

A method for processing a dataset in a partitioned distributed storage system having data stored in a base table and an index stored in an index table, may include receiving base and index table metadata from the partitioned distributed storage system, where the base and index table metadata includes respective table partition information. The method may further include partitioning the dataset into a set of base-delta files according to the base table metadata, and generating a set of index-delta files corresponding with the base-delta files according to the index table metadata. The method may additionally include updating the partitioned distributed storage system with the set of base-delta and the set of index-delta files, where a first update of the base table is synchronous with a second update of the index table.

BACKGROUND

The present disclosure relates to computer software, and morespecifically, to a framework for processing large datasets inpartitioned distributed storage systems.

Distributed storage systems may enable large amounts of data to bestored in clusters of disparate compute nodes. Computer nodes indistributed storage system cluster may be arranged in a master-workerarchitecture with master nodes negotiating transactions with clientapplications and worker nodes executing these transactions. In someembodiments, data may be stored on compute nodes in partitions, orlogical divisions of a storage space.

SUMMARY

According to embodiments of the present disclosure, a method forprocessing a dataset in a partitioned distributed storage system havingdata stored in a base table and an index stored in an index table, mayinclude receiving base and index table metadata from the partitioneddistributed storage system, where the base and index table metadataincludes respective table partition information. The method may furtherinclude partitioning the dataset into a set of base-delta filesaccording to the base table metadata, and generating a set ofindex-delta files corresponding with the base-delta files according tothe index table metadata. The method may additionally include updatingthe partitioned distributed storage system with the set of base-deltaand the set of index-delta files, where a first update of the base tableis synchronous with a second update of the index table.

Various embodiments are directed towards a system for processing adataset in a partitioned distributed storage system having data storedin a base table and an index stored in an index table. The system mayinclude one or more computing nodes having a memory and a processor; anda computer readable storage medium of the one or more computing nodeshaving program instructions embodied therewith, the program instructionsexecutable by the processor to cause the system to: receive base andindex table metadata from the partitioned distributed storage system,wherein the base and index table metadata includes respective tablepartition information; partition the dataset into a set of base-deltafiles according to the base table metadata; generate a set ofindex-delta files corresponding with the base-delta files according tothe index table metadata; and update the partitioned distributed storagesystem with the set of base-delta and the set of index-delta files,where a first update of the base table is synchronous with a secondupdate of the index table.

According to various embodiments, a computer program product forprocessing a dataset in a partitioned distributed storage system havingdata stored in a base table and an index stored in an index table, thecomputer program product including a computer readable storage mediumhaving program instructions embodied therewith, wherein the computerreadable storage medium is not a transitory signal per se, the programinstructions executable by a processing circuit to cause the processingcircuit to perform a method comprising: receiving base and index tablemetadata from the partitioned distributed storage system, wherein thebase and index table metadata includes respective table partitioninformation; partitioning the dataset into a set of base-delta filesaccording to the base table metadata; generating a set of index-deltafiles corresponding with the base-delta files according to the indextable metadata; and updating the partitioned distributed storage systemwith the set of base-delta and the set of index-delta files, where afirst update of the base table is synchronous with a second update ofthe index table.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 depicts a flowchart of a computer implemented method forprocessing a dataset in a partitioned distributed storage system,according to various embodiments.

FIG. 2 depicts a second flowchart of a computer implemented method forprocessing a dataset in a partitioned distributed storage system,according to various embodiments.

FIG. 3 depicts a block diagram of distributed storage systemenvironment, according to various embodiments.

FIG. 4 depicts a cloud computing node according to various embodimentsof the present invention.

FIG. 5 depicts a cloud computing environment, according to variousembodiments of the present invention.

FIG. 6 depicts abstraction model layers according to various embodimentsof the present invention.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to computer software, moreparticular aspects relate to a framework for processing large datasetsin partitioned distributed storage systems. While the present disclosureis not necessarily limited to such applications, various aspects of thedisclosure may be appreciated through a discussion of various examplesusing this context.

Embodiments of the present disclosure are based on the recognition thatsome distributed storage systems (DSS) are built on the append onlyaccess model. The append only access model improves the efficiency ofdistributed storage systems by enabling a DSS design to take advantageof fast sequential disk writes against slow random writes. Instead ofupdating data records in place via random writes, this type of DSSappends a new version of the data record with new timestamp to signalthe deletion of the old data record. Later on, a background sweepingmechanism reclaims the storage space of deleted data records when theirnew versions are present. The efficiency improvements may manifest insimplified solutions to data coherency requirements, and high dataaccess throughput. Client applications' interactions with the DSS mayaffect the efficiency improvements. A client application, for example,may perform batch updates of a DSS. Batch updates may include operationsto load (e.g., add) or delete a dataset (e.g. a collection of data) froma DSS. In a DSS where data is stored in tables (e.g., database tables),a dataset may include one or more tables of the storage system. Suchappend only DSS design has been proven its value in the open source datastores such as in Hadoop-like data stores, including Apache Hadoop,Apache HBase, Apache Accumulo and commercial databases such as IBMDB2-BLU and SAP HANA.

Various embodiments are directed towards a method for processing adataset (e.g., performing batch updates of a dataset) in a partitioneddistributed storage system (PDSS) having data and an index stored intables (e.g., base and index tables, respectively). The processingincludes updating the base tables of the PDSS with a large dataset whilesynchronously updating the index tables with index deltas correspondingwith the dataset. The method may start by receiving metadata about thebase and index tables. The dataset may then be partitioned according tobase table partitioning and formatting requirements indicated in thebase table metadata. Additionally, index deltas corresponding with thepartitioned dataset may be generated according to index partitioning andformatting requirements indicated in the index table metadata.Partitions of the base and index tables may then be incrementallyupdated with the dataset partitions and generated index deltas.

Other embodiments are directed towards a system and computer programproduct for processing a dataset in partitioned distributed storagesystem where data is stored in tables.

As used herein, partitioned distributed storage systems are distributedstorage systems where data is stored in partitions. A partition can be alogical (or physical) division of a storage space. A single compute nodein a cluster may have one or more partitions. Datasets stored on a PDSSmay be partitioned into files or blocks (e.g., a partition may includeone or more files or blocks) according to one or more partitioningcriteria, including key ranges or attribute values of the dataset. Anattribute may be any property of a dataset, including for examplemetadata, while a key may be an identifier of the dataset derived fromthe dataset and/or associated attribute values. Disparate partitioneddata files or blocks may contain non-overlapping data (e.g., data from afirst file or block in a first partition may not be logically part ofdata from a second file or block in a second partition). In someembodiments, partitions may vary in size (e.g., number of bytes) andformat (e.g., fire storage format).

As used herein, metadata may include data about a partitioneddistributed storage system. A first type of metadata (e.g. base tablemetadata) may describe the base table of a PDSS, while a second type ofmetadata (e.g., index table metadata) may describe the index table. Basetable metadata may include data about the size, locations, storageformat, and/or partition criteria of base table partitions. Similarly,index table metadata may include metadata concerning the size,locations, storage format, and/or partition criteria of index tablepartitions. The metadata associated with a PDSS may change as datasetsare updated on the storage system due to, for example, the addition ordeletion of partitions.

Data stored on a partitioned distributed storage system may be stored ina base table. Records in the base table may include elements of thepartitioned DSS, including files and partitions. In some embodiments,data in a PDSS base table partition may be stored in one or morepartition file(s).

Data stored in the base table may be accessed by indexing. Indexingincludes creation of a table (e.g., an index table) of lookup valuesthat may be used to locate data on a storage system. The lookup valuesmay be derived from one or more attribute value of the indexed data. Thelookup values may also be derived from keys corresponding with theindexed data.

The base and index tables of a PDSS may be stored in multiple separatepartitions (e.g., partition files) of the PDSS, and may be updated whenthe system is updated with a dataset. When a base table is updated witha dataset, an index table corresponding with the base table may besynchronously (e.g., occurring substantially at the same time) updatedwith a set of index-deltas (e.g. data references to be added to ordeleted from an index table) associated with the dataset.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to the figures, FIG. 1 depicts a flowchart of a computerimplemented method 100 for processing a dataset in a partitioneddistributed storage system, according to various embodiments. The method100 may be automatically carried out by a storage system managementscript or application controlling a partitioned distributed storagesystem. The PDSS may be implemented in a cloud computing environmentsuch as the cloud computing environment shown FIG. 5. The storage systemmanagement script (hereinafter, the storage system) may reside on one ormore compute nodes in the cloud computing environment, such as computersystem 10 shown in FIG. 4. In some embodiments, the storage system maylock (e.g., prevent compute nodes and applications other than thestorage system from updating) at least a partition of the base and indextables of the PDSS to ensure that the partition topology of the PDSSremains stable (e.g., unchanged) while executing the method 100.

The storage system may begin the method 100 at operation 105 byreceiving a request from a client application to perform an update (e.g.a batch update) of the partitioned distributed storage system. Theupdate request may include a requested update operation and a dataset.In some embodiments, the update operation may be a dataset load or adelete operation. A load operation may be a request to add a dataset tothe PDSS, while a delete operation may be a request to remove a datasetfrom the PDSS.

The storage system may continue the method 100 by executing operation110, receiving base and index table metadata from the PDSS. Inembodiments where the PDSS is configured according to a master-workerarchitecture, the base and index table metadata may reside in a memoryof a master compute node. In other embodiments, the base and index tablemetadata may be distributed in worker compute nodes, with one or moreworker compute nodes having a portion or all of the metadata. Thestorage system may receive the metadata in response to a request formetadata sent from the storage system to one or more compute nodes. Incertain embodiments, the storage system may receive the metadataautomatically in response to receiving and/or processing an updaterequest. Receiving the metadata may include reading the metadata from anetwork port or accessing an area of memory of a compute node executingthe storage system management script.

In some embodiments, the base and index table metadata may includepartition information, respectively, about the base and index tables.The partition information may include information about the sizes,locations, storage formats, and/or partition criteria for base and indextable partitions. Partition size metadata specify the sizes in, forexample, bytes, of a PDSS base and index partitions. Location metadatamay identify the compute node associated with a given partition alongwith the location on the compute node of the partition. Storage formatmetadata may specify the format of partition files, while partitioncriteria may specify how partitions are allocated throughout the PDSS(e.g., the key or attribute value ranges allocated to a givenpartition). Base table partition sizes, formats, and/or partitioncriteria differ from those of index tables. Additionally, partitionsizes, formats, and/or partition criteria may vary within and/or betweencompute nodes.

The storage system may continue the method 100 by executing operation115. Executing operation 115 may include partitioning a dataset into aset of base-delta files according to the base table metadata. Abase-delta file may correspond with (e.g., may be sized, formatted andallocated to fit) a base table partition of the PDSS that may be updatedwith the base-delta file. When the PDSS update is a load operation, abase delta-file may contain at least a portion of the dataset, and maybe map to at least a portion of base table partition. When the PDSSupdate is a delete operation, a base-delta file may be a reference to atleast a portion of a base table partition where the delete will occur(e.g., the base-delta file may identity the partition files or blocks todelete).

The storage system may use base table metadata to determine the size,and format of the base-delta files. The storage system may also use basetable metadata to determine the base table partitions that may beupdated with the base-delta file. In some embodiments, the storagesystem may partition a dataset to reduce the likelihood of individualbase-delta files having overlapping data (e.g., data in a firstbase-delta file is substantially independent from data in a secondbase-delta file). In other embodiments the storage system may partitionthe base-delta files according to any other criteria specified in thebase table metadata.

The method 100 may be further continued by executing operation 120,generating a set of index-delta files corresponding with the base-deltafiles. An index-delta file may correspond with at least a portion of anindex table partition that may be updated with the index-delta file. Theindex-delta file may also contain references to at least a portion ofthe dataset as allocated to the base-delta files. In some embodiments,the storage system may generate the index-delta files by firstdetermining (e.g., from the index table metadata) a criteria forgenerating index-deltas (e.g., a criteria for determining how referencesor keys should be assigned) from the base-deltas generated in operation115. The storage system may then generate the index-deltas and use indextable metadata to determine the size, format and other partitioncriteria to allocate (e.g., partition) the index-deltas to one or moreindex-delta files. The storage system may also use index table metadatato determine the index table partitions that may be updated with theindex-delta files. In some embodiments, the index-delta files may begenerated substantially in parallel with the partitioning of the datasetinto base-delta files. In these embodiments, a first one or more nodesof the PDSS may generate the index-delta files while a second one ormore nodes of the PDSS partitions the dataset into base-delta files. Inother embodiments, the index delta files may be generated subsequent tothe partitioning of the dataset into base-delta files. In particularembodiments, partition information about the base-delta files may informthe generation of the index delta files.

The storage system may continue the method 100 at operation 125 byupdating the partitioned distributed storage system with the base-deltaand index-delta files. When the PDSS update request is for a loadoperation, executing operation 125 may include incrementally updatingthe PDSS by copying the base-delta and index-delta files, respectively,into existing partitions of the base and index tables which physicallyreside in respective compute nodes. In some embodiments new partitionsmay be created in the base and index tables, with the base-delta andindex-delta files being subsequently copied into the newly createdpartitions. In embodiments where the base-delta and index-delta filesare partitioned to reduce the likelihood of overlap between files,incrementally updating the PDSS may include one or more compute nodescopying the base-delta and index-delta files to the PDSS in parallel.The base and index table metadata may be also be updated to reflect theaddition of newly added delta files.

When the partitioned distributed storage system update is a deleteoperation request, operation 125 may include incrementally deleting atleast a portion of the base and index table partitions referenced by thebase-delta and index files. Similar to the load operation, the base andindex table metadata may be also be updated to reflect the deletion.

The method 100 may end at operation 135. In some embodiments, thestorage system may end the method 100 unlocking the base and indextables.

FIG. 2 depicts a second flowchart of a computer implemented method 200for processing a dataset in a partitioned distributed storage system,according to various embodiments. The method 200 may be automaticallycarried out by a storage system management script or applicationcontrolling a PDSS. The PDSS may be implemented in a cloud computingenvironment such as the cloud computing environment shown FIG. 5. Thestorage system management script may reside on one or more compute nodesin the cloud computing environment, such as computer system 10 shown inFIG. 4. In some embodiments, the storage system may lock at least apartition of the base and index tables to ensure that the partitiontopology of the PDSS remains stable (e.g., unchanged) while executingthe method 200.

The storage system may begin the method 200 at operation 205 byreceiving a PDSS update request, as described herein. When the PDSSupdate request is for a load operation, the storage system may lock thebase and index tables to prevent updates to the PDSS by other processes.When the PDSS update request is for a delete operation, the storagesystem may partially lock the base and index tables (e.g., the storagesystem may prevent region splits, merges, and data movement).

The storage system may continue the method 200 at operation 210 byreceiving base and index table metadata. The storage system may receivebase and index table metadata according to the steps described duringthe discussion of operation 110 of the method 100.

The storage system may then execute operation 215 by partitioning adataset into a set of one or more base-delta files. The storage systemmay partition the dataset into a set of one or more base delta filesaccording to the steps described during the discussion of operation 115of the method 100.

The storage system may then proceed to operation 220 and generate a setof one or more index-delta files corresponding with the base-deltafiles. The storage system may generate a set of one or more index-deltafiles according to the steps described during the discussion ofoperation 120 of the method 100.

The storage system may then continue the method 200 at operation 225 bydetermining whether the PDSS update is a load operation. The storagesystem may proceed to operation 230 when the PDSS update is a loadoperation. Alternatively, the storage system may proceed to operation240 when the PDSS update is not a load operation (e.g., the update is adelete operation).

The storage system may perform operation 230 (e.g., perform the loadoperation) by merging (e.g., copying) the base-delta and index-deltafiles from operations 215 and 220 into respective partitions of the baseand index tables, as described in operation 125 of the method 100. Thestorage system may then proceed to operation 235.

When the storage system determines at operation 225 that the PDSS updateis not load operation, the storage system may perform operation 240 bydetermining whether the index delta files generated in operation 220were merged in to the index table partitions. When an index-delta fileis copied to an index table partition during a load operation, theindex-delta may exist as an individual block in an index tablepartition. During the course of operation of the storage system, anindex-delta file may be merged with one or more other index files,creating a new unified index file. The unified index file may containdata marked for deletion (e.g., an index-delta file) and data not markedfor deletion. Operation 240 determines whether the index-delta filesidentified for deletion still exists as an individual data blocks orfiles.

When the index-delta files have not been merged, the storage manager mayperform operation 245 and delete the index-delta files from the indextable partitions. When the index-delta file have been merged, thestorage manager may perform operation 250 and generate delete markersfiles in the index partitions having the merged index-delta files. Adelete markers file may indicate to the storage system to treat blockslisted in the delete markers file (e.g., where a block corresponds withan index-delta file) as if they were deleted from the system. Thestorage system may proceed to operation 255 after executing operation245 or 250.

The storage system may perform operation 255 by determining whether thebase-delta files generated in operation 215 were merged in to the basetable partitions of the PDSS. When a base-delta file is copied to a basetable partition during a load operation, the base-delta file may existas an individual block in a base table partition. During the course ofexecution of the storage system, a base-delta file may be merged withone or more other base table files, creating a new unified base tablefile. The unified base table file may contain data marked for deletion(e.g., a base-delta file) and data not marked for deletion. Operation255 determines whether the base-delta files identified for deletionstill exists as an individual data blocks or files.

When the base-delta files have not been merged, the storage manager mayperform operation 260 and delete the base-delta files from the basetable partitions. When the base-delta files have been merged, thestorage manager may perform operation 265 and generate delete markersfiles in the base table partitions having the merged base-delta files.The storage system may proceed to operation 235 after executingoperation 260 or 265.

The storage system may continue the method 200 at operation 235 byupdating the base and index metadata to reflect the addition or deletionof the base-delta and index-delta files. During a load update, base andindex table metadata may be updated to, for example, include referencesto the base-delta and index-delta files. Additionally, the base andindex table metadata may be updated to indicate the creation of one ormore base and/or index table partitions when new partitions are createdto accommodate the base-delta and index delta files. When the PDSSupdate is a delete operation, the base and index table metadata, may beupdated to indicate, for example, the deletion of one or more baseand/or index table partitions. The base and index table metadata mayalso be updated to indicate the creation of one or more delete markersfiles, as described herein.

The method 200 may end at operation 270. In some embodiments, thestorage system may end the method 200 by unlocking the base and indextables.

FIG. 3 depicts a block diagram of a partitioned distributed storagesystem environment 300, according to various embodiments. Thepartitioned distributed storage system environment 300 includespartitioned distributed storage system client 305 and partitioneddistributed storage system 310. In some embodiments, the partitioneddistributed system environment 300 may be a cloud computing environmentsuch as the cloud computing environment shown in FIG. 5, where thepartitioned distributed storage system client 305 (herein after client305) interacts with the PDSS 310 through a communications network.

Client 305 may be a compute node such as computer system 10 shown inFIG. 4 or a virtual client executing, for example, a data analyticsprocessing application as shown in FIG. 6. Client 305 may be configuredto negotiate data transactions (e.g., data reads, loads, and deletes)with PDSS 310. Client 305 may generate dataset 305D and transmit thedataset over a communications network in a negotiated operation to, forexample, update the PDSS 310.

In some embodiments, partitioned distributed storage system 310 may be aPDSS such as an Apache Hadoop Distributed File System or InternationalBusiness Machines' General Parallel File System File PlacementOptimizer. The PDSS 310 may include metadata module 315, datasetpartitioning module 320, base partition updating module 325, basemetadata updating module 330, index generating module 335, indexupdating module 340, and index metadata updating module 345. PDSS 310may be embodied in a cluster(s) or one or more computing nodes in acloud computing environment such as the cloud computing environmentshown FIG. 5. In some embodiments, the PDSS 310 may be a combination ofstorage systems illustrated on both the hardware and software, andvirtualization cloud computing layers shown in FIG. 6. A storage systemmanagement script or application may automatically negotiate datatransactions with the client 305, and may facilitate interactionsbetween the modules of the PDSS 310 to execute the operations of themethods described herein. The storage system management script, and themodules included in the PDSS 310 may exist on a single compute node, ormay be distributed amongst a plurality of compute nodes.

The metadata module 315 may receive a request from the storage system(e.g., the storage system management script) to obtain base and indextable metadata for the PDSS 310. In some embodiments, the metadatamodule 315 may obtain the base and index table metadata from a computenode configured to aggregate and maintain the PDSS 310 metadata (e.g., amaster compute node). In other embodiments, metadata module 315 mayobtain the metadata by querying a plurality of compute nodes of the PDSS310. Metadata module 315 may provide the metadata to the storage systemby writing it to a file or an area of memory of a compute node that isaccessible to the storage system.

The dataset partitioning module 320 may receive the dataset 305D, alongwith base table metadata from the storage system. The datasetpartitioning module 320 may be configured to partition the dataset 305Dinto one or more base-delta files 320D. The one or more base-delta files320D may be partitioned as described herein, with the datasetpartitioning module 320 allocating portions of the dataset 305D to theone or more base-delta files according to the base table partitionsizes, formats, and other partitioning criteria specified in the basetable metadata. The dataset partitioning module 320 may provide the oneor more base-delta files 320D to the storage system.

The base partition updating module 325 may receive the one or morebase-delta files 320D from the storage system for updating one or morebase table partitions. In some embodiments, the base partition updatingmodule 325 may be distributed amongst one or more compute nodes of thePDSS, with at least one compute node receiving one or more of thebase-delta files 320D. Distributing the base partition updating module325 amongst one or more compute nodes may enable the PDSS to be updatedwith multiple with multiple base-delta files in parallel (e.g., a firstnode be updated with a first base-delta file concurrently with a secondnode being updated with a second base-delta file). The base partitionupdating module 325 may be configured to copy (e.g., during a loadupdate operation) the one or more base-delta files 320D into existingbase table partitions of the at least one compute node. Additionally,the base partition updating module 325 may be configured to create newbase table partitions on the at least one compute node(s) when existingbase table partitions cannot accommodate the one or more base-deltafiles 320D.

The base partition updating module 325 may also be configured to delete(e.g., during a delete update operation) one or more partitions (or datablocks) identified by the one or more base-delta files 320D from atleast one compute node of the PDSS. In some embodiments, base partitionupdating module may be further configured to generate one or more deletemarkers files in the base table partitions identified by the one or morebase-delta files 320D. The base partition updating module 325 mayprovide the updated base table partition files 325D to the storagesystem. The updated base table partition files 325D may include existingbase table partitions (e.g., lightly hatched boxes) and the base-deltafiles (e.g., heavily hatched boxes).

The base metadata updating module 330 may receive updated partitiontopology information (e.g., including size, format), along withpartition file attributes and metadata from the storage system. In someembodiments, the base metadata updating module may be configured toupdate base table metadata or generate new base table metadata toinclude the updated partition topology information, partition fileattributes and metadata. The base metadata updating module 330 mayprovide the new or updated base table metadata to the storage system.

The index generating module 335 may receive the dataset 305D, along withindex table metadata from the storage system. In some embodiments, theindex generating module 335 may be configured to generate one or moreindex-delta files 335D corresponding with the dataset 305D. Theindex-delta files may be generated as described herein, with the indexgenerating module 335 allocating ranges (or portions) of indices of thedataset 305D to the index-delta files 335D according to the index tablepartition size, format, and other partitioning criteria specified in theindex table metadata. The index generating module 335 may provide theindex-delta files 335D to the storage system.

The index updating module 340 may receive the index-delta files 335Dfrom the storage system for merging with one or more index tablepartitions. In some embodiments, index updating module 340 may bedistributed amongst a one or more compute nodes of the PDSS, with atleast one node receiving one or more of the index-delta files 335D.Distributing the index partition updating module 325 and the index-deltafiles 335D amongst one or more compute nodes may enable the PDSS to beupdated with multiple index-delta files in parallel. The index updatingmodule 340 may be configured to copy (e.g., during a load updateoperation) the one or more index-delta files 335D into existing indextable partitions of the at least one compute node. Additionally, theindex updating module 340 may be configured to create new index tablepartitions on the at least one compute node when existing index tablepartitions cannot accommodate the one or more index-delta files 335D.The index updating module 340 may also be configured to delete one ormore partitions (or data blocks) identified by the one or moreindex-delta files 335D from the at least one compute node. In someembodiments, index updating module 340 may be further configured togenerate one or more delete markers files in the index partitionsidentified by the index-delta files 335D. The index updating module 340may provide the updated index table partition files 340D to the storagesystem. The updated index table partition files 340D may includeexisting index table partitions (e.g., heavily hatched boxes) and theindex-delta files (e.g., lightly hatched boxes).

The index metadata update module 345 may receive updated index tablepartition topology information (e.g., size, and format) and partitionfile attributes and metadata from the storage system. In someembodiments, the index metadata update module 345 may be configured toupdate index table metadata or generate new index table metadata toinclude the updated partition topology information (e.g., size, andformat) and partition file attributes and metadata. The index metadataupdate module 345 may provide the new or updated index table metadata tothe storage system.

In FIG. 4, a schematic of an example of a cloud computing node is shown.Cloud computing node 10 is only one example of a suitable cloudcomputing node and is not intended to suggest any limitation as to thescope of use or functionality of embodiments of the invention describedherein. Regardless, cloud computing node 10 is capable of beingimplemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 4, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes; RISC(Reduced Instruction Set Computer) architecture based servers; storagedevices; networks and networking components. In some embodiments,software components include network application server software.

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and mobile desktop.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for processing a dataset in apartitioned distributed storage system having data stored in a basetable and an index stored in an index table, comprising: receiving baseand index table metadata from the partitioned distributed storagesystem, wherein the base and index table metadata includes respectivetable partition information; partitioning the dataset into a set ofbase-delta files according to the base table metadata; generating a setof index-delta files corresponding with the base-delta files accordingto the index table metadata; and updating the partitioned distributedstorage system with the set of base-delta and the set of index-deltafiles, wherein a first update of the base table is synchronous with asecond update of the index table.
 2. The method of claim 1, wherein thepartitioning comprises: determining from the base table metadata apartitioning criteria; and allocating the dataset to the set ofbase-delta files according to a partitioning criteria.
 3. The method ofclaim 1, wherein the generating comprises: determining from the indextable metadata a criteria for generating index-deltas from a set of basetable deltas, wherein the set of base table deltas include the datasetas allocated to the base-delta files, and the set of index-deltasinclude references to the set of base table deltas; generating theindex-deltas according to the criteria for generating index-deltas; anddetermining from the index table metadata a partitioning criteria forallocating the set of index deltas to the set of index-delta files;allocating the set of index-deltas to the set of index-delta filesaccording to the partitioning criteria.
 4. The method of claim 1,wherein the updating comprises: merging the set of base-delta and theset of index-delta files into a respective one or more correspondingpartitions of the base and index tables; and updating the base and indextable metadata in the partitioned distributed storage system.
 5. Themethod of claim 1, wherein the updating comprises: deleting the set ofbase-delta and set of index-delta files in a respective one or morecorresponding partitions of the base and index tables; updating the baseand index table metadata in the partitioned distributed storage system.6. The method of claim 5, wherein the deleting comprises: determiningwhether the set of base-delta files have been merged with respectivepartitions of the base table; deleting, in response to determining thatthe set of base-delta files have not been merged, the set of base-deltafiles from the respective partitions of the base table; generating, inresponse to determining that the set of base-delta files have beenmerged, delete markers files corresponding with the set of base-deltafiles in respective partitions of the base table.
 7. The method of claim5, wherein the deleting comprises: determining whether the set ofindex-delta files have been merged with respective partitions of theindex table; deleting, in response to determining that the set ofindex-delta files have not been merged, the set of index-delta filesfrom the respective partitions of the index table; generating, inresponse to determining that the set of index-delta files have beenmerged, delete markers files corresponding with the set of index-deltafiles in respective partitions of the index table.
 8. The method ofclaim 4, wherein the base table is updated in parallel with the indextable.
 9. The method of claim 4, wherein a plurality of partitioned basetables and index tables are updated in parallel.
 10. The method of claim1, wherein the partitioned distributed storage system is a Hadoop-likedistributed file system.
 11. A system for processing a dataset in apartitioned distributed storage system having data stored in a basetable and an index stored in an index table, comprising: one or morecomputing nodes having a memory and a processor; and a computer readablestorage medium of the one or more computing nodes having programinstructions embodied therewith, the program instructions executable bythe processor to cause the system to: receive base and index tablemetadata from the partitioned distributed storage system, wherein thebase and index table metadata includes respective table partitioninformation; partition the dataset into a set of base-delta filesaccording to the base table metadata; generate a set of index-deltafiles corresponding with the base-delta files according to the indextable metadata; and update the partitioned distributed storage systemwith the set of base-delta and the set of index-delta files, wherein afirst update of the base table is synchronous with a second update ofthe index table.
 12. The system of claim 11, wherein the programinstructions executable by the processor further causes the system to:determine from the base table metadata a partitioning criteria; andallocate the dataset to the set of base-delta files according to apartitioning criteria.
 13. The system of claim 11, wherein the programinstructions executable by the processor further causes the system to:determine from the index table metadata a criteria for generatingindex-deltas from a set of base table deltas, wherein the set of basetable deltas include the dataset as allocated to the base-delta files,and the set of index-deltas include references to the set of base tabledeltas; generate the index-deltas according to the criteria forgenerating index-deltas; and determine from the index table metadata apartitioning criteria for allocating the set of index deltas to the setof index-delta files; allocating the set of index-deltas to the set ofindex-delta files according to the partitioning criteria.
 14. The systemof claim 11, wherein the program instructions executable by theprocessor further causes the system to: merge the set of base-delta andthe set of index-delta files into a respective one or more correspondingpartitions of the base and index tables; and update the base and indextable metadata in the partitioned distributed storage system.
 15. Thesystem of claim 11, wherein the program instructions executable by theprocessor further causes the system to: delete the set of base-delta andset of index-delta files in a respective one or more correspondingpartitions of the base and index tables; update the base and index tablemetadata in the partitioned distributed storage system.
 16. A computerprogram product for processing a dataset in a partitioned distributedstorage system having data stored in a base table and an index stored inan index table, the computer program product including a computerreadable storage medium having program instructions embodied therewith,wherein the computer readable storage medium is not a transitory signalper se, the program instructions executable by a processing circuit tocause the processing circuit to perform a method comprising: receivingbase and index table metadata from the partitioned distributed storagesystem, wherein the base and index table metadata includes respectivetable partition information; partitioning the dataset into a set ofbase-delta files according to the base table metadata; generating a setof index-delta files corresponding with the base-delta files accordingto the index table metadata; and updating the partitioned distributedstorage system with the set of base-delta and the set of index-deltafiles, wherein a first update of the base table is synchronous with asecond update of the index table.
 17. The computer program product ofclaim 16, wherein the program instructions executable by the processorfurther causes the system to: determining from the base table metadata apartitioning criteria; and allocating the dataset to the set ofbase-delta files according to a partitioning criteria.
 18. The computerprogram product of claim 16, wherein the generating comprises:determining from the index table metadata a criteria for generatingindex-deltas from a set of base table deltas, wherein the set of basetable deltas include the dataset as allocated to the base-delta files,and the set of index-deltas include references to the set of base tabledeltas; generating the index-deltas according to the criteria forgenerating index-deltas; and determining from the index table metadata apartitioning criteria for allocating the set of index deltas to the setof index-delta files; allocating the set of index-deltas to the set ofindex-delta files according to the partitioning criteria.
 19. Thecomputer program product of claim 16, wherein the updating comprises:merging the set of base-delta and the set of index-delta files into arespective one or more corresponding partitions of the base and indextables; and updating the base and index table metadata in thepartitioned distributed storage system.
 20. The computer program productof claim 16, wherein the updating comprises: deleting the set ofbase-delta and set of index-delta files in a respective one or morecorresponding partitions of the base and index tables; updating the baseand index table metadata in the partitioned distributed storage system.